Tininnimiutait — characterization of the nutritional value, safety, and taste of edible brown seaweeds and blue mussels for encouraging a sustainable, healthy, and tasty diet in Nunavik communities
Bibliographic record
Abstract
Cet ensemble de données a été produit dans le cadre du projet "Assessing the Potential of Local Marine Foods Accessible from the Shore to Increase Food Security and Sovereignty in Nunavik", dirigé par Lucie Beaulieu de l'Université Laval. Le projet a été financé par Sentinelle Nord de 2020 à 2024. Les données ont été récoltées dans le but de caractériser la valeur nutritive, le goût et l'innocuité d'algues brunes (Alaria esculenta, Fucus spp. et Saccharina spp.) et de la moule bleue (Mytilus spp.) récoltées dans différentes régions de la baie d'Ungava au Nunavik. Il s'agit de données numériques, de figures, tableaux et d'analyses statistiques. Ces données servent à déterminer si ces produits marins sont favorables à une alimentation saine et si leur consommation présente des risques pour la santé.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".